AI systems can now draft, summarize, analyze, explain, translate, and solve. These capabilities can widen access to knowledge, reduce barriers, and support people with difficult tasks.

They also change the conditions under which human abilities are practiced.

Better performance with assistance is not necessarily the same as stronger independent capability. The distinction matters wherever people are expected not only to produce an answer, but to understand it, evaluate it, adapt it, and take responsibility for its consequences.

The changing scope of cognitive offloading

Cognitive offloading involves using an external tool to carry part of a mental task. People have always done this.

Writing stores memory outside the mind. Calculators reduce the need for mental arithmetic. Search engines change what people try to remember and what they expect to retrieve later.

AI extends offloading into activities that have traditionally involved interpretation, reasoning, composition, and judgment. That expansion matters because these activities are not only outputs. They are also practices through which people develop understanding and expertise.

When performance and capability diverge

An AI system may improve the quality or speed of an immediate task while reducing the amount of reasoning a person performs themselves.

A person may complete an assignment, produce a report, or reach a plausible answer without developing the knowledge needed to verify it, adapt it, or reproduce the reasoning unaided.

Recent research offers an early warning, not a final verdict. Gerlich’s 2025 study of 666 participants found an association between frequent AI tool use, cognitive offloading, and lower critical-thinking scores. The study was correlational, so it cannot establish that AI use caused the difference.

Fan and colleagues’ research similarly indicates that generative AI can influence learners’ motivation and metacognitive processes. Their findings suggest that the consequences depend substantially on how the tool is used and how learning is structured.

These studies do not establish a universal effect, nor do they prove that AI use inevitably weakens human capability. They do support a careful distinction: AI assistance can improve task performance while also reducing opportunities for the effortful practice through which independent capability develops.

Designing use around human capability

The relevant choice is not simply whether to use AI. It is how the work is organized around it.

Use that prompts explanation, comparison, revision, verification, and reflection can keep the person engaged in the reasoning. Use that supplies an answer before the person has formed a view can make acceptance easier than understanding.

The same system can therefore support learning in one setting and weaken it in another.

A student might use AI to generate counterarguments and test their reasoning, or to produce an essay they cannot explain. A professional might use it to surface possibilities and then verify them against primary evidence, or accept a fluent summary without checking what was omitted.

The technology matters, but so do the surrounding expectations, time pressures, assessment practices, and institutional norms.

Expertise is more than output